Course Recommendation from Social Data

Hana Bydžovská, Lubomír Popelínský

2014

Abstract

This paper focuses on recommendations of suitable courses for students. For a successful graduation, a student needs to obtain a minimum number of credits that depends on the field of study. Mandatory and selective courses are usually defined. Additionally, students can enrol in any optional course. Searching for interesting and achievable courses is time-consuming because it depends on individual specializations and interests. The aim of this research is to inspect different techniques how to recommend students such courses. This paper brings results of experiments with three approaches of predicting student success. The first one is based on mining study-related data and social network analysis. The second one explores only average grades of students. The last one aims at subgroup discovery for which prediction may be more reliable. Based on these findings we can recommend courses that students will pass with a high accuracy.

Paper Citation

in Harvard Style

Bydžovská H. and Popelínský L. (2014). Course Recommendation from Social Data . In Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-020-8, pages 268-275. DOI: 10.5220/0004840002680275

in Bibtex Style

@conference{csedu14,
author={Hana Bydžovská and Lubomír Popelínský},
title={Course Recommendation from Social Data},
booktitle={Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2014},
pages={268-275},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004840002680275},
isbn={978-989-758-020-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Course Recommendation from Social Data
SN - 978-989-758-020-8
AU - Bydžovská H.
AU - Popelínský L.
PY - 2014
SP - 268
EP - 275
DO - 10.5220/0004840002680275